VARIABLES DE SALUD
#Limpieza
BASE DE DATOS: DENSIDAD DE CAMAS EN LOS CENTROS HOSPITALARIOS (Mayra Vargas)
library(htmltab)
linkCIA_cama = "https://www.cia.gov/library/publications/resources/the-world-factbook/fields/360.html"
linkPath_cama='//*[@id="fieldListing"]'
camas = htmltab(doc = linkCIA_cama,
which = linkPath_cama)## No encoding supplied: defaulting to UTF-8.#LIMPIEZA
library(tidyr)
camas=separate(camas,`Hospital bed density`,into = c("oficial","delete")," ")## Warning: Expected 2 pieces. Additional pieces discarded in 180 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].camas$delete=NULL
names(camas)=c("Pais","numero de camas por 1000 hab")
camas$`numero de camas por 1000 hab`=as.numeric(camas$`numero de camas por 1000 hab`)
str(camas)## 'data.frame': 182 obs. of 2 variables:
## $ Pais : chr "Afghanistan" "Albania" "Algeria" "Andorra" ...
## $ numero de camas por 1000 hab: num 0.5 2.9 1.9 2.5 3.8 5 4.2 3.8 7.6 4.7 ...#NA
table(camas$`numero de camas por 1000 hab`,useNA = "always")##
## 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6
## 1 2 3 1 3 2 8 6 3 3 4 3 8 5 5 5
## 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2
## 3 1 3 3 4 4 2 2 2 4 6 4 5 3 2 1
## 3.4 3.5 3.6 3.7 3.8 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5
## 4 1 2 3 5 2 1 2 2 2 1 1 4 3 1 3
## 5.2 5.4 5.6 5.7 5.8 5.9 6.2 6.3 6.5 6.7 6.8 7 7.3 7.4 7.6 8.2
## 1 1 1 1 4 1 1 2 3 1 1 2 1 1 1 2
## 8.3 8.7 8.8 11 11.5 13.2 13.4 13.8 <NA>
## 1 1 1 1 1 1 1 1 2#MERGE PARA QUEDARNOS CON LOS PAÍSES DE EUROPA Y AMÉRICA
library(rio)
lkpais = "https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
Paisesoficial=import(lkpais)
Numerodecamas=merge(camas,Paisesoficial,by.x='Pais',by.y='Pais')BASE DE DATOS: GASTO ACTUAL EN SALUD (Current Health Expenditure - CHE) (Mayra Vargas)
library(htmltab)
linkCIA_che = "https://www.cia.gov/library/publications/resources/the-world-factbook/fields/409.html"
linkPath_che='//*[@id="fieldListing"]'
che= htmltab(doc = linkCIA_che,
which = linkPath_che)## No encoding supplied: defaulting to UTF-8.#LIMPIEZA
library(tidyr)
che=separate(che,`Current Health Expenditure`,into = c("oficial","delete"),"%")
che$delete=NULL
names(che)=c("Pais","CHE")
che$CHE=as.numeric(che$CHE)
str(che)## 'data.frame': 193 obs. of 2 variables:
## $ Pais: chr "Afghanistan" "Albania" "Algeria" "Andorra" ...
## $ CHE : num 11.8 6.7 6.4 10.3 2.8 4.5 9.1 10.4 9.2 10.4 ...#NA
table(che$CHE,useNA = "always")##
## 1.2 1.8 2.3 2.4 2.5 2.6 2.8 2.9 3 3.1 3.2 3.3 3.5 3.6 3.7 3.8
## 1 1 1 1 2 1 2 3 1 2 1 6 3 1 2 4
## 3.9 4 4.1 4.2 4.4 4.5 4.7 4.8 4.9 5 5.2 5.3 5.5 5.6 5.7 5.8
## 2 2 2 2 3 6 4 2 2 3 5 4 5 3 1 3
## 5.9 6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7 7.2 7.3 7.4 7.5
## 3 2 3 4 2 4 2 2 5 2 4 3 7 2 3 1
## 7.6 7.7 7.9 8 8.1 8.2 8.3 8.4 8.6 8.7 8.8 8.9 9 9.1 9.2 9.3
## 3 1 1 2 3 3 2 1 3 1 2 2 3 1 3 2
## 9.5 9.6 9.8 9.9 10 10.1 10.3 10.4 10.6 10.8 10.9 11 11.2 11.3 11.7 11.8
## 1 2 1 1 1 2 2 3 1 1 1 2 1 1 1 1
## 12 12.3 12.4 13.4 16.4 17.1 <NA>
## 1 1 1 1 1 2 1#MERGE PARA QUEDARNOS CON LOS PAÍSES DE EUROPA Y AMÉRICA
library(rio)
lkpaises= "https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
Paisesoficial=import(lkpaises)
HealthExpenditure=merge(che,Paisesoficial,by.x='Pais', by.y='Pais')Base de datos: Asistencia sanitaria universal (Gonzalo Berger)
library(rio)
UHClk='https://github.com/GonzaloBerger/123/raw/master/Data_Extract_From_World_Development_Indicators.xlsx'
datauhc=import(UHClk)
lkpaises='https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls'
datapaises=import(lkpaises)
datauhc[,c(1:2,4:13,15:16)]=NULL
datauhc=datauhc[-c(218:269),]
names(datauhc)=c('Pais','Asistencia sanitaria universal')
datauhc$`Asistencia sanitaria universal`=as.numeric(datauhc$`Asistencia sanitaria universal`)## Warning: NAs introduced by coerciondatauhc[datauhc$Pais=='Venezuela, RB','Pais']='Venezuela'
datauhc[datauhc$Pais=='Czech Republic','Pais']='Czechia'
datauhc[datauhc$Pais=='Slovak Republic','Pais']='Slovakia'
datauhc[datauhc$Pais=='Russian Federation','Pais']='Russia'#NA (Gonzalo Berger)
table(datauhc$`Asistencia sanitaria universal`, useNA = 'always')##
## 25 28 31 33 37 38 39 40 41 42 43 44 45 46 47 48
## 1 2 1 1 3 2 4 5 3 3 2 2 4 3 5 4
## 49 51 52 53 54 55 57 58 59 60 61 62 63 64 65 66
## 2 1 2 1 1 4 2 2 1 2 5 4 2 3 4 3
## 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83
## 7 6 5 6 4 6 8 5 11 6 4 5 2 2 4 6
## 84 86 87 89 <NA>
## 3 4 4 1 34##MERGE PARA QUEDARNOS CON LOS PAÍSES DE EUROPA Y AMÉRICA LATINA (Gonzalo Berger)
UHC=merge(datapaises,datauhc)BASE DE DATOS: OCUPACIÓN HOSPITALARIA (Ivonne Mondoñedo Mora)
library(htmltab)
LinkDoc="https://www.cia.gov/library/publications/resources/the-world-factbook/fields/359.html"
Link_path_doc='//*[@id="fieldListing"]'
Medicos= htmltab (doc = LinkDoc,which = Link_path_doc)## No encoding supplied: defaulting to UTF-8.#Limpieza
names(Medicos)## [1] "Country" "Physicians density"names(Medicos)= c ("Pais", "Numero_medicos")
library(tidyr)
Medicos=separate(Medicos,Numero_medicos,into=c("Numero_medicos",'delete'), "\\ ")[,-3]## Warning: Expected 2 pieces. Additional pieces discarded in 198 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].Medicos$Numero_medicos=as.numeric(Medicos$Numero_medicos)
Medicos$Numero_medicos=trimws(Medicos$Numero_medicos,whitespace = "[\\h\\v]")
Medicos$Numero_medicos=as.numeric(Medicos$Numero_medicos)#Ver los NA’s
table(Medicos$`Numero_medicos`,useNA = "always")##
## 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.16 0.17 0.18
## 2 1 2 5 2 3 3 4 1 2 1 1 1 1 3 3
## 0.19 0.2 0.21 0.22 0.23 0.28 0.31 0.34 0.36 0.37 0.38 0.4 0.41 0.46 0.5 0.52
## 1 4 1 1 2 1 2 1 2 2 2 1 1 1 1 1
## 0.53 0.65 0.66 0.72 0.73 0.77 0.78 0.79 0.8 0.81 0.82 0.84 0.86 0.91 0.92 0.93
## 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1
## 0.95 0.96 0.98 1.01 1.04 1.08 1.13 1.14 1.15 1.19 1.2 1.22 1.23 1.24 1.27 1.28
## 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1
## 1.3 1.32 1.37 1.42 1.45 1.51 1.56 1.57 1.61 1.7 1.76 1.77 1.79 1.83 1.87 1.88
## 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1
## 1.89 1.94 1.95 1.96 1.97 2.05 2.08 2.13 2.15 2.16 2.2 2.22 2.25 2.26 2.27 2.31
## 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1
## 2.33 2.34 2.37 2.39 2.4 2.41 2.46 2.49 2.52 2.54 2.58 2.59 2.61 2.62 2.67 2.72
## 1 1 2 2 1 2 1 1 1 1 1 1 1 1 1 1
## 2.76 2.78 2.81 2.87 2.89 2.9 3.01 3.03 3.09 3.13 3.19 3.2 3.22 3.23 3.25 3.32
## 1 1 2 1 2 1 1 2 1 2 1 1 1 2 1 1
## 3.33 3.34 3.45 3.47 3.51 3.59 3.67 3.81 3.83 3.96 3.97 3.99 4.01 4.07 4.08 4.09
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 4.21 4.24 4.31 4.34 4.46 4.59 4.63 5.05 5.1 5.14 5.4 6.15 6.56 8.19 <NA>
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4#Merge para quedarnos solo con países de Europa y América
library(rio)
Europa_America3="https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
EuroAmer3=import(Europa_America3)
names(EuroAmer3)= c ("Pais")
Doctores_EA =merge(Medicos,EuroAmer3,by.x='Pais', by.y='Pais') BASE DE DATOS: che_per_capita (Ivonne Mondoñedo Mora)
library(rio)
Linkche_per_capita="https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/Current-health-expenditure-per-capita-current-US.xls"
che_per_capita= import(Linkche_per_capita)#Limpieza
che_per_capita[c(2)]=NULL
names(che_per_capita)= c ("Pais", "Porcentaje_che_per_capita")
library(tidyr)
che_per_capita=separate(che_per_capita,Porcentaje_che_per_capita,into=c("Porcentaje_che_per_capita",'delete'), "\\%")[,-3]## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 231 rows [2, 3,
## 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, ...].che_per_capita$Porcentaje_che_per_capita=trimws(che_per_capita$Porcentaje_che_per_capita,whitespace = "[\\h\\v]")
che_per_capita[,c(2)]=as.numeric(che_per_capita[,c(2)])#Ver los NA’s
table(che_per_capita$`Porcentaje_che_per_capita`,useNA = "always")##
## 19.4316463470459 21.0711555480957 22.8885726928711 23.2723255157471
## 1 1 1 1
## 23.5004329681396 24.1500549316406 24.6708374023438 25.2619533538818
## 1 1 1 1
## 29.2616500854492 29.730863571167 30.7664470672607 31.3781051635742
## 1 1 1 1
## 32.2593841552734 32.9119987487793 33.7201118469238 33.916576385498
## 1 1 1 1
## 36.2823371887207 38.048713684082 38.426441192627 44.403491973877
## 1 1 1 1
## 44.5929641723633 44.8095965381493 47.1952864953533 47.9153633117676
## 1 1 1 1
## 48.8171310424805 49.2044563293457 49.6357703944185 49.9837226867676
## 1 1 1 1
## 52.3594093322754 53.2310967934123 55.0140380859375 55.0745622009202
## 1 1 1 1
## 56.5990295410156 57.8998527526855 58.0439567565918 58.760929107666
## 1 1 1 1
## 61.4578399658203 62.1246337890625 62.3532791137695 64.4653930414567
## 1 1 1 2
## 65.1958396929016 65.896891395763 66.4021835327148 66.7494125366211
## 1 1 1 1
## 67.1226501464844 67.6486663818359 67.8118133544922 69.293098449707
## 1 1 1 1
## 69.7492446899414 70.3308944702148 73.9249801635742 76.6103210449219
## 1 1 1 1
## 78.8228378295898 80.5009177413571 81.409367173405 82.0758666992188
## 1 1 1 1
## 83.0623281282646 83.1977081298828 83.7588180080558 94.229377746582
## 1 1 2 1
## 96.7993011474609 98.824577331543 101.239868164063 104.552795410156
## 1 1 1 1
## 105.666526794434 105.768455505371 110.149620056152 114.45964050293
## 1 1 1 1
## 114.971786499023 119.695655822754 129.575958251953 132.900985717773
## 1 1 1 1
## 148.784454345703 159.484741210938 161.010864257813 167.589614868164
## 1 1 1 1
## 169.716268119907 171.41748046875 177.408889770508 188.414321899414
## 1 1 1 1
## 191.185775756836 192.083389282227 193.793045043945 195.935745239258
## 1 1 1 1
## 199.00904111287 204.492248535156 210.313705444336 220.274566650391
## 1 1 1 1
## 222.015487670898 224.736770629883 230.527282714844 233.065063476563
## 1 1 1 1
## 247.035110473633 249.513676366239 250.562225341797 258.481440267046
## 1 1 1 1
## 258.494293212891 259.935028076172 262.153071659075 269.496705965721
## 1 1 2 1
## 275.809356689453 279.645324707031 280.498809814453 282.491027832031
## 1 1 1 1
## 293.053588867188 301.150054931641 307.196044921875 320.593292236328
## 1 1 1 1
## 324.586792263897 328.419799804688 332.570922851563 339.327972412109
## 1 1 1 1
## 340.661804199219 342.499908447266 344.273677286342 344.289399151357
## 1 1 1 1
## 345.789410458349 381.113067626953 384.066070556641 407.635864257813
## 1 1 1 1
## 410.185185436188 424.809783935547 433.208587646484 439.594451904297
## 1 1 1 1
## 440.825622558594 444.653686523438 447.280639648438 456.482666015625
## 1 1 1 1
## 458.960856789522 459.197570800781 459.918042262602 460.068817138672
## 1 1 1 1
## 460.473327636719 462.715268773023 465.929321289063 475.479949951172
## 1 1 1 1
## 494.677642822266 497.236053466797 499.237548828125 506.216645875853
## 1 1 1 1
## 518.029602050781 528.545166015625 555.104736328125 582.671927003548
## 1 1 1 1
## 585.873229980469 587.646301269531 599.699768066406 602.038208133858
## 1 1 1 1
## 622.176696777344 639.897020371404 642.199157714844 653.926397143921
## 1 1 1 1
## 663.715087890625 671.079956165229 671.411499023438 673.859680175781
## 1 1 1 1
## 678.874847632542 685.31702184882 719.443481445313 791.656677246094
## 1 1 1 1
## 869.077758789063 902.1396484375 902.658996582031 906.820129394531
## 1 1 1 1
## 928.79931640625 930.352355957031 934.008050740366 981.423034667969
## 1 1 1 1
## 987.627014160156 1006.93878173828 1061.14674489701 1078.17919921875
## 1 1 1 1
## 1093.40551757813 1106.7509765625 1112.30322265625 1124.09167480469
## 1 1 1 1
## 1127.18627929688 1183.83618164063 1186.13635253906 1300.48168945313
## 1 1 1 1
## 1324.603515625 1357.01745605469 1381.98620605469 1475.91516113281
## 1 1 1 1
## 1516.58776855469 1529.07763671875 1591.533203125 1596.36291503906
## 1 1 1 1
## 1649.18615722656 1731.69445800781 1771.53552246094 1908.03393554688
## 1 1 1 1
## 1920.28186035156 2192.44307690792 2283.07470703125 2506.46484375
## 1 1 1 1
## 2585.56396484375 2618.71240234375 2840.13061523438 2932.421875
## 1 1 1 1
## 3144.62622070313 3261.42622643507 3361.64477539063 3761.33529352997
## 1 1 1 1
## 3858.67431640625 3937.22192382813 4040.78662109375 4168.986328125
## 1 1 1 1
## 4205.74267578125 4379.72705078125 4507.3564453125 4675.68324696195
## 1 1 1 1
## 4754.94775390625 4911.4404296875 4939.87548828125 4976.8623046875
## 1 1 1 1
## 5033.4521484375 5284.11754994531 5331.81787109375 5550.62460893834
## 1 1 1 1
## 5782.62841796875 5800.1513671875 5904.583984375 6086.3115234375
## 1 1 1 1
## 7936.375 9691.08933947145 9956.259765625 10246.138671875
## 1 1 1 1
## <NA>
## 33#Merge para quedarnos solo con países de Europa y América
library(rio)
Europa_America2="https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
EuroAmer2=import(Europa_America2)
names(EuroAmer2)= c ("Pais")
che_per_capita_EA =merge(che_per_capita,EuroAmer2,by.x='Pais', by.y='Pais') VARIABLES DE GOBERNANZA
Limpieza de las bases de datos
#BASE DE DATOS: Estabilidad y ausencia de violencia + Control de Corrupción (Gonzalo Berger)
library(rio)
lkcorrup='https://github.com/GonzaloBerger/123/raw/master/Data_Extract_From_Worldwide_Governance_control_of_corruption.xlsx'
datacorrup=import(lkcorrup)
lkpaises='https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls'
datapaises=import(lkpaises)
datacorrup[c(2:4)]=NULL
datacorrup=datacorrup[c(1:99),]
names(datacorrup)=c('Pais','Control de la corrupción')
datacorrup$`Control de la corrupción`=as.numeric(datacorrup$`Control de la corrupción`)## Warning: NAs introduced by coercionlkviolence='https://github.com/GonzaloBerger/123/raw/master/Data_Extract_From_Worldwide_Governance_Indicators_political_stability_and_absence_of_violence.xlsx'
dataviolence=import(lkviolence)
dataviolence[c(2:4)]=NULL
dataviolence=dataviolence[c(1:99),]
names(dataviolence)=c('Pais','Estabilidad política y ausencia de violencia/terrorismo')
dataviolence$`Estabilidad política y ausencia de violencia/terrorismo`=as.numeric(dataviolence$`Estabilidad política y ausencia de violencia/terrorismo`)## Warning: NAs introduced by coercionNA
table(datacorrup$`Control de la corrupción`, useNA = 'always')##
## 4.807693 6.25 8.173077 9.615385 12.5 12.98077 16.34615 18.26923
## 1 1 1 1 1 1 1 1
## 18.75 20.67308 21.15385 21.63461 22.11539 24.51923 25.96154 28.84615
## 1 1 1 1 1 1 1 1
## 29.32692 29.80769 31.73077 32.21154 32.69231 34.61538 35.09615 35.57692
## 1 1 1 1 1 1 1 1
## 36.05769 40.38462 41.82692 42.30769 42.78846 43.75 44.23077 44.71154
## 1 1 1 1 1 1 1 1
## 46.63462 48.07692 49.03846 50 50.96154 51.92308 52.40385 54.32692
## 1 1 1 1 1 1 1 1
## 55.76923 58.17308 59.13462 59.61538 60.09615 61.05769 62.01923 63.46154
## 1 1 1 1 1 1 1 1
## 64.42308 66.34615 66.82692 67.78846 68.26923 68.75 69.23077 69.71154
## 1 1 1 1 1 1 1 1
## 70.19231 70.67308 72.59615 73.55769 74.03846 74.51923 76.44231 78.84615
## 1 1 1 1 1 1 1 1
## 80.28846 80.76923 81.25 81.73077 82.69231 83.17308 84.13461 86.53846
## 1 1 1 1 1 1 1 4
## 87.01923 87.5 87.98077 88.46154 88.94231 89.90385 90.38461 90.86539
## 1 1 1 1 1 1 1 1
## 91.34615 93.26923 93.75 94.71154 95.19231 95.67308 96.15385 96.63461
## 1 1 1 1 1 1 1 1
## 97.11539 97.59615 98.07692 98.55769 100 <NA>
## 1 1 1 1 1 3table(dataviolence$`Estabilidad política y ausencia de violencia/terrorismo`, useNA = 'always')##
## 6.190476 9.047619 10 17.61905 18.09524 20 20.95238 23.33333
## 1 1 1 1 1 1 1 1
## 24.28572 25.23809 25.71428 26.66667 27.14286 29.04762 30 30.47619
## 1 1 1 1 1 1 1 1
## 30.95238 31.90476 32.85714 33.33333 35.71429 37.14286 38.09524 39.52381
## 1 1 1 1 1 1 1 1
## 40.95238 42.38095 43.33333 45.23809 45.71429 46.19048 46.66667 47.61905
## 1 1 1 1 1 1 1 1
## 48.09524 48.57143 49.04762 49.52381 50 50.95238 51.90476 54.76191
## 1 1 1 1 1 1 1 1
## 55.23809 56.19048 57.61905 58.09524 58.57143 59.04762 59.52381 60.47619
## 1 1 1 1 1 1 1 1
## 60.95238 61.42857 61.90476 62.38095 62.85714 63.80952 64.7619 65.71429
## 1 1 1 1 1 1 1 1
## 66.19048 66.66666 67.61905 70 70.47619 72.38095 72.85714 73.33334
## 1 1 1 1 1 1 1 1
## 73.80952 76.19048 77.61905 78.09524 78.57143 80 80.47619 80.95238
## 1 1 1 1 1 1 1 1
## 81.42857 81.90476 82.38095 84.28571 84.7619 85.2381 85.71429 86.19048
## 1 1 1 2 1 1 1 1
## 87.14286 87.61905 89.04762 89.52381 90 90.47619 90.95238 93.33334
## 1 1 1 1 1 1 1 1
## 94.28571 95.2381 95.71429 96.19048 96.66666 97.61905 98.09524 99.52381
## 1 1 1 1 1 1 1 1
## 100 <NA>
## 1 1Merge
corrupcionyviolencia=merge(datacorrup,dataviolence)
CORRUPVIOLENCIA=merge(datapaises,corrupcionyviolencia) Base de datos de la base de datos: voz y Accountability: rango de porcentaje (Voice and Accountability: PErcentile Rank) Mayra Vargas
library(rio)
lXvoandac="https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/Data_From_Worldwide_Governance_Indicators_Voice%20and%20Accountability%20Percentile%20Rank.xlsx"
VoiceandAcc=import(lXvoandac)Limpieza
VoiceandAcc[,c(2,3,4)]=NULL
names(VoiceandAcc)=c("Pais","VoiceandAccountability")
VoiceandAcc$VoiceandAccountability=as.numeric(VoiceandAcc$VoiceandAccountability)## Warning: NAs introduced by coercionstr(VoiceandAcc)## 'data.frame': 99 obs. of 2 variables:
## $ Pais : chr "Albania" "Andorra" "Anguilla" "Antigua and Barbuda" ...
## $ VoiceandAccountability: num 53.2 83.3 NA 69.5 67 ...NA
table(VoiceandAcc$VoiceandAccountability,useNA = "always")##
## 0.9852217 4.926108 6.403941 7.881773 9.852217 10.34483 11.82266 15.76355
## 1 1 1 1 1 1 1 1
## 18.71921 19.21182 25.12315 26.60098 31.52709 33.99015 35.46798 37.43842
## 1 1 1 1 1 1 1 1
## 39.90148 40.39409 40.8867 44.33498 44.82759 45.3202 45.81281 46.30542
## 1 1 1 1 1 1 1 1
## 47.29064 48.27586 49.26109 50.2463 51.23153 52.70936 53.20197 55.17241
## 1 1 1 1 1 1 1 1
## 56.15763 56.65025 58.12808 58.62069 59.1133 60.59113 61.08374 61.57635
## 1 1 1 1 1 1 1 1
## 64.03941 64.53202 65.51724 66.99507 67.48769 68.47291 68.96552 69.45813
## 2 1 1 1 1 1 1 1
## 71.42857 71.92118 73.39902 74.38424 74.87685 75.36946 75.86207 76.35468
## 1 1 1 1 1 1 1 1
## 76.84729 77.3399 77.83251 78.32513 79.31035 80.78818 81.28078 81.7734
## 1 1 1 1 1 1 1 1
## 82.26601 82.75862 83.25123 84.23645 84.72906 87.68473 88.17734 88.66995
## 1 1 1 1 1 1 1 1
## 89.16256 89.65517 91.62562 92.11823 92.61084 93.10345 93.59606 94.08867
## 1 1 4 1 1 1 1 1
## 94.58128 95.07389 96.05911 96.55173 97.04433 97.53695 98.02956 98.52217
## 1 1 1 1 1 1 1 1
## 99.01478 100 <NA>
## 1 1 5Merge
library(rio)
lkpaises = "https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
Paisesoficial=import(lkpaises)
VoiceandAccountability = merge(VoiceandAcc,Paisesoficial,by.x='Pais',by.y='Pais')BASE DE DATOS: Efectivida del gobierno - Ivonne Deliany Mondoñedo Mora
library(rio)
Gobernabilidad="https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/Data_Extract_From_Worldwide_Governance_Indicators%20(3).xlsx"
Gobernanza=import(Gobernabilidad)Limpieza
names(Gobernanza)= c ("Pais", "Efectividad_gobierno")
Gobernanza$Efectividad_gobierno=as.numeric(Gobernanza$Efectividad_gobierno)## Warning: NAs introduced by coercionGobernanza$Efectividad_gobierno=trimws(Gobernanza$Efectividad_gobierno,whitespace = "[\\h\\v]")
Gobernanza[Gobernanza$Pais=='Russian','Pais']='Russia'Ver los NA’s
table(Gobernanza$`Efectividad_gobierno`,useNA = "always")##
## 1.442308 12.01923 14.42308 19.23077 23.55769 25.48077 27.88461 28.36539
## 1 1 1 1 1 1 1 1
## 28.84615 30.28846 33.65385 34.13462 35.57692 36.05769 36.53846 37.98077
## 1 1 1 1 1 1 1 1
## 38.46154 39.42308 39.90385 4.807693 40.38462 40.86538 41.82692 42.78846
## 1 1 1 1 1 1 1 1
## 43.26923 43.75 44.23077 44.71154 47.11538 47.59615 49.03846 50
## 1 1 1 1 1 1 1 1
## 50.96154 51.44231 51.92308 52.88462 53.84615 54.32692 54.80769 55.76923
## 1 1 1 1 1 1 1 1
## 56.73077 57.69231 58.17308 60.57692 61.53846 62.98077 65.86539 67.78846
## 1 1 1 1 2 1 1 1
## 68.26923 68.75 69.23077 70.19231 70.67308 71.63461 72.59615 73.07692
## 1 1 1 1 1 1 1 1
## 74.03846 75 75.48077 76.44231 76.92308 77.88461 78.36539 79.32692
## 1 1 1 2 1 1 1 1
## 79.80769 80.28846 80.76923 81.73077 82.69231 83.17308 83.65385 84.61539
## 1 1 1 1 1 1 1 1
## 85.57692 86.53846 87.5 87.98077 88.94231 89.42308 89.90385 90.86539
## 1 1 1 1 1 1 1 1
## 91.34615 91.82692 92.30769 93.26923 94.71154 95.19231 95.67308 96.15385
## 1 1 1 1 1 1 1 1
## 96.63461 97.11539 97.59615 98.55769 99.03846 99.51923 <NA>
## 1 1 1 1 1 1 3Merge para quedarnos solo con países de Europa y América
library(rio)
Europa_America4="https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
EuroAmer4=import(Europa_America4)
names(EuroAmer4)= c ("Pais")
Gobernanza_EA=merge(Gobernanza,EuroAmer4,by.x='Pais', by.y='Pais') Base de datos de estado de derecho: Rango de Percentil (Rule of Law: Percentil Rank)- Mayra Vargas
library(rio)
lkRofLaw= "https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/Data_From_Worldwide_Governance_Indicators_Rule%20of%20Law%20Percentile%20Rank.xlsx"
RuleofLaw= import(lkRofLaw)Limpieza
RuleofLaw[,c(2,3,4)] = NULL
names(RuleofLaw)=c("Pais","RuleofLaw")
RuleofLaw$RuleofLaw=as.numeric(RuleofLaw$RuleofLaw)## Warning: NAs introduced by coercionNA
table(RuleofLaw$RuleofLaw,useNA = "always")##
## 0 6.25 7.692307 9.615385 12.98077 13.46154 14.90385 15.86539
## 1 1 1 1 1 1 1 1
## 16.34615 17.78846 19.23077 19.71154 20.19231 20.67308 24.03846 27.40385
## 1 1 1 1 1 1 1 1
## 28.84615 29.32692 32.21154 32.69231 35.09615 35.57692 37.01923 38.46154
## 1 1 1 1 1 1 1 1
## 38.94231 39.42308 40.38462 41.82692 42.30769 43.75 44.23077 45.67308
## 1 1 1 1 1 1 1 1
## 46.15385 46.63462 48.55769 49.03846 50.48077 51.44231 51.92308 52.40385
## 1 1 1 1 1 1 1 1
## 53.36538 57.21154 57.69231 59.13462 61.53846 62.5 62.98077 63.46154
## 1 1 1 1 1 1 1 1
## 63.94231 65.38461 65.86539 66.82692 67.30769 69.23077 70.19231 71.15385
## 1 1 1 1 1 1 1 1
## 72.11539 72.59615 73.07692 73.55769 74.03846 75.96154 76.44231 78.84615
## 1 1 1 1 1 1 1 2
## 79.32692 79.80769 80.28846 81.73077 82.69231 83.65385 84.61539 85.09615
## 1 1 1 1 1 1 1 1
## 85.57692 86.53846 87.01923 88.46154 88.94231 89.42308 89.90385 90.86539
## 1 1 1 1 1 1 1 1
## 91.34615 91.82692 92.30769 93.26923 94.23077 94.71154 95.67308 96.15385
## 1 1 1 1 2 1 1 1
## 96.63461 97.59615 98.55769 99.03846 99.51923 100 <NA>
## 1 1 1 1 1 1 3#Merge para quedarnos con los países de Europa y América Latina
lkpaises = "https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
Paisesoficial=import(lkpaises)
Imperiodelaley=merge(RuleofLaw,Paisesoficial,by.x = 'Pais',by.y = 'Pais')Base de dato regulatory quality - Mayra Vargas
library(rio)
lkregulatoryq= "https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/Regulatory%20Quality%20-%20Percentile%20Rank.xlsx"
Regqual= import(lkregulatoryq)Limpieza
Regqual[,c(2,3,4)] = NULL
names(Regqual)=c("Pais","RegulatoryQuality")Regqual[Regqual$Pais=='Venezuela, RB','Pais']='Venezuela'
Regqual$RegulatoryQuality=as.numeric(Regqual$RegulatoryQuality)## Warning: NAs introduced by coercionstr(Regqual)## 'data.frame': 99 obs. of 2 variables:
## $ Pais : chr "Albania" "Andorra" "Anguilla" "Antigua and Barbuda" ...
## $ RegulatoryQuality: num 63.5 85.1 77.4 66.8 42.3 ...NA
table(Regqual$RegulatoryQuality,useNA = "always")##
## 0.4807692 2.884615 6.25 8.653846 12.01923 12.5 15.86539 16.34615
## 1 1 1 1 1 1 1 1
## 25 25.96154 26.44231 28.84615 31.73077 32.21154 34.61538 37.01923
## 1 1 1 1 1 1 1 1
## 37.98077 39.90385 40.38462 41.34615 42.30769 44.23077 45.19231 45.67308
## 1 1 1 1 1 1 1 1
## 48.55769 50.48077 51.92308 52.40385 53.84615 55.28846 56.25 57.69231
## 1 1 1 1 1 1 1 1
## 60.09615 60.57692 61.05769 62.01923 62.5 62.98077 63.46154 63.94231
## 1 1 1 1 1 1 1 1
## 64.42308 65.38461 65.86539 66.34615 66.82692 67.30769 68.26923 69.23077
## 1 1 1 1 1 1 1 1
## 69.71154 70.19231 71.15385 71.63461 72.11539 72.59615 73.07692 73.55769
## 1 1 1 1 1 1 1 1
## 75 75.48077 75.96154 77.40385 77.88461 78.36539 78.84615 79.32692
## 1 1 1 3 1 1 1 1
## 80.28846 80.76923 81.25 82.69231 83.17308 83.65385 85.09615 85.57692
## 1 1 1 1 1 1 2 1
## 86.05769 87.01923 87.5 88.94231 89.90385 90.38461 90.86539 91.34615
## 1 1 1 1 1 1 1 1
## 91.82692 92.30769 92.78846 93.75 94.23077 94.71154 95.19231 95.67308
## 1 1 1 1 1 1 1 1
## 96.15385 96.63461 97.11539 97.59615 99.03846 <NA>
## 1 1 1 1 1 3MERGE
lkpaises = "https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
Paisesoficial=import(lkpaises)
RegulatoryQuality=merge(Regqual,Paisesoficial,by.x='Pais', by.y='Pais')#VARIABLES DE ACCESO A AGUA POTABLE Y SANEAMIENTO # Limpieza #Base: Acceso a agua potable (Gonzalo Berger)
library(htmltab)
library(tidyr)
library(stringr)
library(rio)lkpage='https://www.cia.gov/library/publications/resources/the-world-factbook/fields/361.html'
lkpath='//*[@id="fieldListing"]'
Aguapotable = htmltab(doc = lkpage, which = lkpath)## No encoding supplied: defaulting to UTF-8.lkpaises='https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls'
datapaises=import(lkpaises)
Aguapotable = separate(Aguapotable,'Drinking water source',into=c('Z1','Z2'), 'total')## Warning: Expected 2 pieces. Additional pieces discarded in 220 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [58].Aguapotable[,c(2)]=NULL
Aguapotable = separate(Aguapotable,'Z2',into=c('Z1','Z2'),'%')## Warning: Expected 2 pieces. Additional pieces discarded in 196 rows [1, 2, 3, 5,
## 6, 7, 9, 10, 11, 12, 13, 14, 17, 19, 20, 21, 22, 24, 25, 26, ...].Aguapotable[,c(3)]=NULL
Aguapotable[,c(2)] = gsub(':','',Aguapotable[,c(2)])
Aguapotable[,c(2)] = trimws(Aguapotable[,c(2)],whitespace = '[\\h\\v]')
Aguapotable$Z1=as.numeric(Aguapotable$Z1)
names(Aguapotable)=c('Pais','% of population with access to improved drinking water')NA
table(Aguapotable$`% of population with access to improved drinking water`, useNA = 'always')##
## 31.7 40 51.1 51.5 54.9 55.2 55.5 55.6 55.7 57.8 57.9 58.2 58.7 62.6 63.1 64.4
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 65.4 65.8 67.6 68 68.5 68.9 70.2 71.1 71.6 71.9 73.5 73.8 75 75.6 76.1 76.4
## 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1
## 76.5 76.9 77 78.2 78.3 78.5 79 79.2 79.9 80.3 80.8 81 81.8 82.1 83.7 85.4
## 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1
## 86.7 87 87.1 87.3 88.4 89 89.3 89.9 90.1 90.2 90.3 90.8 91 91.1 91.4 91.6
## 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1
## 91.8 92 92.7 92.8 93.1 93.2 93.4 93.6 93.8 94 94.1 94.2 94.5 94.6 94.7 94.8
## 1 1 1 2 1 1 1 1 2 1 1 2 2 1 1 2
## 95.1 95.2 95.3 95.6 95.7 96 96.1 96.2 96.3 96.5 96.6 96.7 96.8 96.9 97 97.1
## 2 1 1 1 1 1 1 3 1 1 1 3 3 2 1 1
## 97.2 97.3 97.4 97.5 97.6 97.7 97.8 97.9 98 98.1 98.2 98.3 98.4 98.5 98.6 98.7
## 1 1 5 2 1 2 1 1 1 1 2 2 1 4 3 1
## 98.9 99 99.1 99.2 99.4 99.5 99.6 99.7 99.8 99.9 100 <NA>
## 2 3 1 3 1 1 2 3 1 2 60 1Merge
Agua=merge(datapaises,Aguapotable)BASE DE DATOS: Saneamiento - Ivonne Deliany Mondoñedo Mora
library(htmltab)
Linksana="https://www.cia.gov/library/publications/resources/the-world-factbook/fields/398.html"
Link_path_sana='//*[@id="fieldListing"]'
saneamiento= htmltab (doc = Linksana,which = Link_path_sana)## No encoding supplied: defaulting to UTF-8.Limpieza
names(saneamiento)= c ("Pais", "Porcentaje_Saneamiento")
library(tidyr)
saneamiento$Porcentaje_Saneamiento=gsub("\\%.*","",saneamiento$Porcentaje_Saneamiento)
saneamiento$Porcentaje_Saneamiento=gsub(".*\\:","",saneamiento$Porcentaje_Saneamiento)
saneamiento$Porcentaje_Saneamiento=as.numeric(saneamiento$Porcentaje_Saneamiento)
saneamiento$Porcentaje_Saneamiento=trimws(saneamiento$Porcentaje_Saneamiento,whitespace = "[\\h\\v]")Ver los NA’s
table(saneamiento$`Porcentaje_Saneamiento`,useNA = "always")##
## 100 16.4 18 20 20.2 22.8 24.7 27.2 28 28.5 31.2 31.3 31.4 32.8 33.5 33.6
## 21 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1
## 34.1 35.6 37.3 37.5 37.9 40.8 42.4 43.4 43.6 43.8 43.9 44.5 45.1 47.3 48.3 49.3
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 50.4 51.2 52 54.5 55.6 56 56.4 57.5 57.7 58.5 59.8 60.8 61.5 61.8 62.5 62.6
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 63.1 65.1 65.4 65.6 66.4 69 69.6 72.3 76.1 76.5 77 77.5 77.9 78.5 79.6 79.7
## 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1
## 79.9 80.7 81.4 81.6 82.3 82.4 82.5 82.9 83.1 83.5 84.1 84.3 84.5 84.7 85.1 85.2
## 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 86.2 86.3 86.4 86.6 86.7 86.8 87 87.3 87.8 87.9 88 88.1 88.4 88.6 89.1 89.8
## 1 1 1 1 1 1 1 1 1 2 2 2 1 1 2 2
## 89.9 90.5 90.8 91.4 91.5 91.6 92 92.2 92.5 92.8 93 93.3 93.4 93.5 93.8 93.9
## 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1
## 94.1 94.4 94.5 95.2 95.5 95.6 96 96.1 96.2 96.4 96.6 96.8 97 97.2 97.3 97.4
## 1 2 1 2 2 1 1 1 4 1 1 2 1 2 1 2
## 97.5 97.6 97.7 97.8 97.9 98 98.2 98.3 98.4 98.5 98.6 98.7 98.9 99.1 99.2 99.3
## 8 2 1 2 1 4 1 1 1 1 2 1 1 3 2 3
## 99.4 99.5 99.6 99.8 99.9 <NA>
## 2 2 2 1 1 0Merge para quedarnos solo con países de Europa y América
library(rio)
Europa_America1="https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
EuroAmer1=import(Europa_America1)
names(EuroAmer1)= c ("Pais")
Saneamiento_EA =merge(saneamiento,EuroAmer1,by.x='Pais', by.y='Pais')Limpieza: Muertes confirmadas por millón de habitantes (Gonzalo Berger)
library(htmltab)
library(rio)
library(stringr)lkpage='https://en.wikipedia.org/wiki/COVID-19_pandemic_by_country_and_territory'
lkpath='//*[@id="thetable"]'
lkpage2='https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)'
lkpath2='//*[@id="main"]'
covid19=htmltab(doc=lkpage,which = lkpath)## Warning: Columns [Location,Ref.] seem to have no data and are removed. Use
## rm_nodata_cols = F to suppress this behaviorpoblacion=htmltab(doc=lkpage2,which = lkpath2)
lkpaises='https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls'
datapaises=import(lkpaises)
covid19[,c(2,4)]=NULL
poblacion[,c(4,6)]=NULL
names(covid19)[names(covid19)=='Location >> World']= 'Pais'
names(poblacion)[names(poblacion)=='Country/Territory >> World']= 'Pais'
poblacion$Pais=str_split(poblacion$Pais,pattern='\\(',simplify = T)[,1]
poblacion$Pais=trimws(poblacion$Pais,whitespace = "[\\h\\v]")
poblacion$Pais=gsub('Â',"",poblacion$Pais)
poblacion$Pais=trimws(poblacion$Pais,whitespace = "[\\h\\v]")
names(covid19)=c('Pais','Muertes_confirmadas')
covid19$Muertes_confirmadas=gsub(',','',covid19$Muertes_confirmadas)
covid19$Muertes_confirmadas=gsub('No data',NA,covid19$Muertes_confirmadas)
covid19=covid19[-c(229:230),]
covid19[,c(2)]=as.numeric(covid19[,c(2)])
names(poblacion)=c('Pais','continentalregion','subregion','Poblacion_2019')
poblacion$Poblacion_2019=gsub(',',"",poblacion$Poblacion_2019)
poblacion$Poblacion_2019=as.numeric(poblacion$Poblacion_2019)
covid19[covid19$Pais=='Saint Vincent','Pais']='Saint Vincent and the Grenadines'NA
table(covid19$Muertes_confirmadas, useNA = 'always')##
## 0 1 2 3 4 5 6 7 8 9 10
## 30 9 7 5 3 2 1 3 2 3 4
## 11 13 14 15 16 17 19 20 22 23 24
## 3 2 2 4 1 1 2 1 1 1 1
## 26 27 29 32 35 41 42 43 46 49 50
## 3 2 1 1 2 1 1 1 2 3 1
## 52 53 54 56 58 59 60 61 64 67 69
## 2 1 1 1 2 1 1 1 1 2 2
## 74 75 80 82 83 87 93 102 103 106 109
## 1 2 1 1 1 1 1 1 1 1 1
## 114 116 119 124 125 141 145 147 150 151 157
## 1 1 1 1 1 1 1 1 1 1 2
## 161 164 174 182 196 206 212 215 219 255 274
## 1 1 1 1 1 1 1 1 1 1 1
## 301 329 339 341 351 353 374 382 387 422 441
## 1 1 1 1 1 1 1 1 1 1 1
## 447 448 486 493 512 559 573 596 615 718 738
## 1 1 1 1 1 1 1 1 1 1 1
## 746 778 793 879 1006 1160 1210 1272 1312 1372 1421
## 1 1 1 1 1 1 1 1 1 1 1
## 1693 1705 1716 1735 1763 1924 2023 2343 2866 2894 3111
## 1 1 1 1 1 1 1 1 1 1 1
## 3543 4634 4741 4805 5131 5657 5691 5743 5951 6147 8005
## 1 1 1 1 1 1 1 1 1 1 1
## 8935 9224 9457 9840 10105 13963 16766 19021 28445 30265 35141
## 1 1 1 1 1 1 1 1 1 1 1
## 35747 46000 46119 92568 155660 <NA>
## 1 1 1 1 1 1Merge y estandarización
data=merge(covid19,poblacion)
data$'Decesos por millón de habitantes'=(data$Muertes_confirmadas/data$Poblacion_2019)*10^6
data=data[,-c(2,5)]
dataCovid=merge(data,datapaises)Merge de las variables de salud (Gonzalo Berger)
camasCHE = merge(Numerodecamas,HealthExpenditure,all.x=T,all.y=T)
UHCcamasCHE = merge(UHC,camasCHE,all.x=T,all.y=T)
DoctoresUHCcamasCHE = merge(Doctores_EA,UHCcamasCHE,all.x=T,all.y=T)
DataSalud = merge (che_per_capita_EA,DoctoresUHCcamasCHE,all.x=T,all.y=T)MERGE DE VARIABLES DE GOBERNANZA - Ivonne Mondoñedo Mora
Accountability_Corrupcion_Violencia=merge(CORRUPVIOLENCIA,VoiceandAccountability,all.x=T, all.y=T)
Gob_Ley=merge(Gobernanza_EA,Imperiodelaley,all.x=T, all.y=T)
Accountability_Corrupcion_Violencia_Gob_Ley=merge(Accountability_Corrupcion_Violencia,Gob_Ley, all.x=T, all.y=T)
Merge_Gobernanza=merge(Accountability_Corrupcion_Violencia_Gob_Ley,RegulatoryQuality, all.x=T, all.y=T)
Merge_Gobernanza$Efectividad_gobierno=as.numeric(Merge_Gobernanza$Efectividad_gobierno)Merge de las variables de saneamiento y agua potable - Mayra Vargas
Aguaysaneamiento=merge(Agua,Saneamiento_EA,all.x =T,all.y =T)
Aguaysaneamiento$Porcentaje_Saneamiento=as.numeric(Aguaysaneamiento$Porcentaje_Saneamiento)MERGE FINAL (Mayra Vargas)
merge1=merge(DataSalud,Merge_Gobernanza,all.x = T,all.y = T)
merge2=merge(Aguaysaneamiento,merge1,all.x = T,all.y = T)Datacovidof=merge(dataCovid,merge2,all.x = T,all.y = T)names(Datacovidof)## [1] "Pais"
## [2] "continentalregion"
## [3] "subregion"
## [4] "Decesos por millón de habitantes"
## [5] "% of population with access to improved drinking water"
## [6] "Porcentaje_Saneamiento"
## [7] "Porcentaje_che_per_capita"
## [8] "Numero_medicos"
## [9] "Asistencia sanitaria universal"
## [10] "numero de camas por 1000 hab"
## [11] "CHE"
## [12] "Control de la corrupción"
## [13] "Estabilidad política y ausencia de violencia/terrorismo"
## [14] "VoiceandAccountability"
## [15] "Efectividad_gobierno"
## [16] "RuleofLaw"
## [17] "RegulatoryQuality"names(Datacovidof)=c("Pais","Region","Subregion","Decesos","Agua","Saneamiento","CHE_percapita","Medicos","UHC","Camas","CHE","CC","PV","VA","GE","RL","RQ")Clusters de salud (Gonzalo Berger)
No jarárquico - Partición
library(cluster)
library(factoextra)## Loading required package: ggplot2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(dbscan)
library(fpc)##
## Attaching package: 'fpc'## The following object is masked from 'package:dbscan':
##
## dbscanset.seed(777)
row.names(Datacovidof)=Datacovidof$Pais
Datacovidof=na.omit(Datacovidof)
g.distsalud = daisy(Datacovidof[,c(7:11)], metric='gower')
fviz_nbclust(Datacovidof[,c(7:11)], pam,diss=g.distsalud,method = 'gap_stat',k.max = 10,verbose = F)
res.pamSALUD=pam(g.distsalud,2,cluster.only = F)
Datacovidof$clusterPAM=res.pamSALUD$cluster
fviz_cluster(object = list(data=g.distsalud, cluster = Datacovidof$clusterPAM),
geom = c('text'),
ellipse.type = 'convex')
Jerarquización
fviz_nbclust(Datacovidof[,c(4,7:11)], hcut,diss=g.distsalud,method = 'gap_stat',k.max = 10,verbose = F)
Aglomerativo - Agnes
res.agSALUD = hcut(g.distsalud, k = 2,hc_func='agnes',hc_method = 'ward.D')
Datacovidof$clusterAG=res.agSALUD$cluster
fviz_dend(res.agSALUD,k=2, cex = 0.5, horiz = T)
Divisivo - Diana
res.diaSALUD = hcut(g.distsalud, k = 2,hc_func='diana')
Datacovidof$clusterDIV=res.diaSALUD$cluster
fviz_dend(res.diaSALUD, cex = 0.5,horiz = T)
Evaluación gráfica con siluetas
fviz_silhouette(res.pamSALUD)## cluster size ave.sil.width
## 1 1 49 0.44
## 2 2 18 0.40
fviz_silhouette(res.agSALUD)## cluster size ave.sil.width
## 1 1 46 0.46
## 2 2 21 0.37
fviz_silhouette(res.diaSALUD)## cluster size ave.sil.width
## 1 1 46 0.46
## 2 2 21 0.37
Evaluación numérica
poorPAMSALUD=data.frame(res.pamSALUD$silinfo$widths)
poorPAMSALUD$Pais=row.names(poorPAMSALUD)
poorPAMcasesSALUD=poorPAMSALUD[poorPAMSALUD$sil_width<0,'Pais']
poorPAMcasesSALUD## [1] "Ireland" "Uruguay" "Portugal"length(poorPAMcasesSALUD)## [1] 3poorAGNESSALUD=data.frame(res.agSALUD$silinfo$widths)
poorAGNESSALUD$Pais=row.names(poorAGNESSALUD)
poorAGNEScasesSALUD=poorAGNESSALUD[poorAGNESSALUD$sil_width<0,'Pais']
poorAGNEScasesSALUD## character(0)length(poorAGNEScasesSALUD)## [1] 0poorDIANASALUD=data.frame(res.diaSALUD$silinfo$widths)
poorDIANASALUD$Pais=row.names(poorDIANASALUD)
poorDIANAcasesSALUD=poorDIANASALUD[poorDIANASALUD$sil_width<0,'Pais']
poorDIANAcasesSALUD## character(0)length(poorDIANAcasesSALUD)## [1] 0Estrategia basada en densiadad
proyeccionSALUD = cmdscale(g.distsalud, k=2,add = T)
Datacovidof$dim1 = proyeccionSALUD$points[,1]
Datacovidof$dim2 = proyeccionSALUD$points[,2]
min(Datacovidof[,c('dim1','dim2')]); max(Datacovidof[,c('dim1','dim2')])## [1] -0.4521162## [1] 0.3723024limites=c(-0.5,0.4)
g.dist.cmdSALUD = daisy(Datacovidof[,c('dim1','dim2')], metric = 'euclidean')
kNNdistplot(g.dist.cmdSALUD, k=5)
abline(h=0.13, lty=2)
db.cmdSALUD = dbscan(g.dist.cmdSALUD, eps=0.13, MinPts=5,method = 'dist')
db.cmdSALUD ## dbscan Pts=67 MinPts=5 eps=0.13
## 1
## border 3
## seed 64
## total 67CLUSTERS DE GOBERNANZA - Ivonne Mondoñedo
library(cluster)
library(factoextra)
set.seed(777)
row.names(Datacovidof)=Datacovidof$Pais
Datacovidof=na.omit(Datacovidof)
g.dist_gob= daisy(Datacovidof[,c(12,17)], metric="gower")##Cluster no jerarquico - Partición
fviz_nbclust(Datacovidof[,c(12,17)], pam,diss=g.dist_gob,method = "gap_stat",k.max = 10,verbose = F)
res.pam_gob=pam(g.dist_gob,2,cluster.only = F)
Datacovidof$clusterPT=res.pam_gob$cluster
fviz_cluster(object = list(data=g.dist_gob, cluster = Datacovidof$clusterPT),
geom = c("text"),
ellipse.type = "convex")
#Clusters jerarquicos
fviz_nbclust(Datacovidof[,c(12,17)], hcut,diss=g.dist_gob,method = "gap_stat",k.max = 10,verbose = F)
#Aglomerativo - Agnes
res.agnes_gob = hcut(g.dist_gob, k = 5,hc_func='agnes',hc_method = "ward.D")
Datacovidof$clustAG=res.agnes_gob$cluster
fviz_dend(res.agnes_gob,k=5, cex = 0.5, horiz = T)
#Divisivo - Diana
res.diana_gob = hcut(g.dist_gob, k = 5,hc_func='diana')
Datacovidof$clustDIV=res.diana_gob$cluster
fviz_dend(res.diana_gob, cex = 0.5,horiz = T)
#Evaluación gráfica con siluetas
fviz_silhouette(res.pam_gob)## cluster size ave.sil.width
## 1 1 38 0.48
## 2 2 29 0.73
fviz_silhouette(res.agnes_gob)## cluster size ave.sil.width
## 1 1 15 0.36
## 2 2 7 0.49
## 3 3 12 0.54
## 4 4 18 0.72
## 5 5 15 0.27
fviz_silhouette(res.diana_gob)## cluster size ave.sil.width
## 1 1 15 0.45
## 2 2 23 0.25
## 3 3 18 0.72
## 4 4 10 0.25
## 5 5 1 0.00
#Evaluacion númerica
poorPAM_Gob=data.frame(res.pam_gob$silinfo$widths)
poorPAM_Gob$Pais=row.names(poorPAM_Gob)
poorPAMcases_gob=poorPAM_Gob[poorPAM_Gob$sil_width<0,'Pais']
# osea:
poorPAMcases_gob## [1] "Antigua and Barbuda" "Hungary" "Italy"# agnes
poorAGNES_gob=data.frame(res.agnes_gob$silinfo$widths)
poorAGNES_gob$Pais=row.names(poorAGNES_gob)
poorAGNEScases_gob=poorAGNES_gob[poorAGNES_gob$sil_width<0,'Pais']
poorAGNEScases_gob## [1] "Brazil"#diana:
poorDIANA_gob=data.frame(res.diana_gob$silinfo$widths)
poorDIANA_gob$Pais=row.names(poorDIANA_gob)
poorDIANAcases_gob=poorDIANA_gob[poorDIANA_gob$sil_width<0,'Pais']
poorDIANAcases_gob## [1] "Romania" "Greece" "Grenada" "Bulgaria"#Estrategia basada en densidad
proyeccion_gob= cmdscale(g.dist_gob, k=2,add = T)
Datacovidof$dim1 <- proyeccion_gob$points[,1]
Datacovidof$dim2 <- proyeccion_gob$points[,2]base_gob= ggplot(Datacovidof,aes(x=dim1, y=dim2,label=row.names(Datacovidof)))
base_gob + geom_text(size=2)
Datacovidof$pam=as.factor(res.pam_gob$clustering)
Datacovidof$agnes=as.factor(res.agnes_gob$cluster)
Datacovidof$diana=as.factor(res.diana_gob$cluster)min(Datacovidof[,c('dim1','dim2')]); max(Datacovidof[,c('dim1','dim2')])## [1] -0.7459546## [1] 0.5616711limites=c(-0.8,0.6)
base_gob= ggplot(Datacovidof,aes(x=dim1, y=dim2)) + ylim(limites) + xlim(limites) + coord_fixed()
base_gob + geom_point(size=2, aes(color=pam)) + labs(title = "PAM") 
base_gob + geom_point(size=2, aes(color=agnes)) + labs(title = "AGNES")
base_gob + geom_point(size=2, aes(color=diana)) + labs(title = "DIANA")
library(dbscan)
library(cluster)
g.dist.cmd_gob=daisy(Datacovidof[,c('dim1','dim2')], metric = 'euclidean')
kNNdistplot(g.dist.cmd_gob, k=6)
abline(h=0.23, lty=2)
library(fpc)
db.cmd_gob= dbscan(g.dist.cmd_gob, eps=0.23, MinPts=6,method = 'dist')
db.cmd_gob## dbscan Pts=67 MinPts=6 eps=0.23
## 1
## border 3
## seed 64
## total 67Datacovidof$dbCMD_gob=as.factor(db.cmd_gob$cluster)library(ggrepel)
base_gob= ggplot(Datacovidof,aes(x=dim1, y=dim2)) + ylim(limites) + xlim(limites) + coord_fixed()
dbplot_gob= base_gob + geom_point(aes(color=dbCMD_gob))
dbplot_gob
LABEL=ifelse(Datacovidof$dbCMD_gob==0,row.names(Merge_Gobernanza),"")
dbplot_gob + geom_text_repel(aes(label=LABEL),
size=6,
direction = "y", ylim = 0.45,
angle=45,
segment.colour = "grey")
Clusters de agua potable y saneamiento (Mayra Vargas)
library(cluster)
library(factoextra)
library(stringr)
library(magrittr)##
## Attaching package: 'magrittr'## The following object is masked from 'package:tidyr':
##
## extractset.seed(777)
row.names(Datacovidof)=Datacovidof$Pais
Datacovidof=na.omit(Datacovidof)
distancia= daisy(Datacovidof[,c(5,6)],metric = "gower")Cluster no jerárquico - partición
fviz_nbclust(Datacovidof[,c(5,6)], pam, diss = distancia,method = "gap_stat",k.max = 10,verbose = F)
pam.resultado = pam(distancia,1,cluster.only = F)
Datacovidof$clusterAS=pam.resultado$cluster
fviz_cluster(object = list(data=distancia, cluster = Datacovidof$clusterAS),
geom = c("text"),
ellipse.type = "convex")
Clusters jerarquicos
fviz_nbclust(Datacovidof[,c(5,6)], hcut, diss = distancia,method = "gap_stat",k.max = 10,verbose = F)
#Aglomerativo - Agnes
res.agnes<-hcut(distancia, k = 1,hc_func='agnes',hc_method = "ward.D")
Datacovidof$clusterAgnes=res.agnes$cluster
fviz_dend(res.agnes,k=1, cex = 0.5, horiz = T)
#Divisivo - Diana
res.diana <- hcut(distancia, k = 1,hc_func='diana')
Datacovidof$clusterDivisivo=res.diana$cluster
fviz_dend(res.diana, cex = 0.7,horiz = T)
#Estrategia basada en densidad
proyeccionAS = cmdscale(distancia, k=2,add = T)Datacovidof$dim1 <- proyeccionAS$points[,1]
Datacovidof$dim2 <- proyeccionAS$points[,2]
#limites
min(Datacovidof[,c('dim1','dim2')]); max(Datacovidof[,c('dim1','dim2')])## [1] -0.3484398## [1] 0.9852876limites=c(-0.4,1)
g.dist.cmd_AS = daisy(Datacovidof[,c('dim1','dim2')], metric = 'euclidean')library(dbscan)
kNNdistplot(g.dist.cmd_AS, k=2)
abline(h=0.06,lty=2)
library(fpc)
db.cmd_AS= dbscan(g.dist.cmd_AS, eps=0.06, MinPts=2,method = 'dist')
db.cmd_AS## dbscan Pts=67 MinPts=2 eps=0.06
## 0 1 2
## border 2 0 0
## seed 0 62 3
## total 2 62 3Datacovidof$dbCMD_AS=as.factor(db.cmd_AS$cluster)library(ggrepel)
base_AS= ggplot(Datacovidof,aes(x=dim1, y=dim2)) + ylim(limites) + xlim(limites) + coord_fixed()
dbplot_AS= base_AS + geom_point(aes(color=dbCMD_AS))
dbplot_AS
dbplot_AS + geom_text_repel(size=3,aes(label=row.names(Datacovidof[,c(5,6)])))
LABEL=ifelse(Datacovidof$dbCMD_AS==0,row.names(Datacovidof[,c(5,6)]),"")
dbplot_AS + geom_text_repel(aes(label=LABEL),
size=5,
direction = "y", ylim = 0.45,
angle=45,
segment.colour = "turquoise")
Analisis Factorial Exploratorio (EFA) - Mayra Vargas
Datacovidof=na.omit(Datacovidof)COVIDEFA=Datacovidof[,c(5:17)]library(polycor)
MatrixCovid=polycor::hetcor(COVIDEFA)$correlationsExplorar Correlaciones
Sin evaluar significancia
library(ggcorrplot)
ggcorrplot(MatrixCovid)
Evaluando significancia
ggcorrplot(MatrixCovid,
p.mat = cor_pmat(MatrixCovid),
insig = "blank")
KMO
library(psych)##
## Attaching package: 'psych'## The following object is masked from 'package:polycor':
##
## polyserial## The following objects are masked from 'package:ggplot2':
##
## %+%, alphapsych::KMO(MatrixCovid)## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = MatrixCovid)
## Overall MSA = 0.88
## MSA for each item =
## Agua Saneamiento CHE_percapita Medicos UHC
## 0.82 0.81 0.89 0.84 0.92
## Camas CHE CC PV VA
## 0.80 0.74 0.93 0.90 0.92
## GE RL RQ
## 0.92 0.90 0.85Pruebas
HNula: para matriz identidad
cortest.bartlett(MatrixCovid,n=nrow(COVIDEFA))$p.value>0.05## [1] FALSE#Hnula: para matriz singular
library(matrixcalc)
is.singular.matrix(MatrixCovid)## [1] FALSE#Redimensionar
fa.parallel(COVIDEFA,fm = 'ML', fa = 'fa')
## Parallel analysis suggests that the number of factors = 2 and the number of components = NAPrimer resultado
library(GPArotation)
resulefa=fa(COVIDEFA,nfactors = 2,cor = 'mixed',rotate = "varimax",fm="minres")print(resulefa$loadings)##
## Loadings:
## MR1 MR2
## Agua 0.350 0.732
## Saneamiento 0.333 0.845
## CHE_percapita 0.831 0.141
## Medicos 0.339 0.554
## UHC 0.649 0.374
## Camas 0.556
## CHE 0.496 0.147
## CC 0.864 0.404
## PV 0.720 0.306
## VA 0.895 0.178
## GE 0.861 0.432
## RL 0.894 0.370
## RQ 0.787 0.334
##
## MR1 MR2
## SS loadings 5.936 2.771
## Proportion Var 0.457 0.213
## Cumulative Var 0.457 0.670Resultado mejorado
print(resulefa$loadings,cutoff = 0.5)##
## Loadings:
## MR1 MR2
## Agua 0.732
## Saneamiento 0.845
## CHE_percapita 0.831
## Medicos 0.554
## UHC 0.649
## Camas 0.556
## CHE
## CC 0.864
## PV 0.720
## VA 0.895
## GE 0.861
## RL 0.894
## RQ 0.787
##
## MR1 MR2
## SS loadings 5.936 2.771
## Proportion Var 0.457 0.213
## Cumulative Var 0.457 0.670Visualizamos
fa.diagram(resulefa)
Evaluación
¿La Raíz del error cuadrático medio corregida está cerca a cero?
resulefa$crms## [1] 0.08239574¿La Raíz del error cuadrático medio de aproximación es menor a 0.05?
resulefa$RMSEA## RMSEA lower upper confidence
## 0.1762054 0.1476160 0.2094905 0.9000000¿El índice de Tucker-Lewis es mayor a 0.9?
resulefa$TLI## [1] 0.8023441¿Qué variables aportaron mas a los factores?
sort(resulefa$communality)## CHE Camas Medicos UHC PV
## 0.2679057 0.3138029 0.4217295 0.5606282 0.6117942
## Agua CHE_percapita RQ Saneamiento VA
## 0.6588169 0.7101397 0.7310945 0.8247676 0.8328148
## CC GE RL
## 0.9089611 0.9286548 0.9358971¿Qué variables contribuyen a mas de un factor?
sort(resulefa$complexity)## Camas CHE_percapita VA CHE Saneamiento
## 1.027863 1.057921 1.078813 1.174052 1.302415
## RL RQ PV CC Agua
## 1.332106 1.347796 1.349005 1.416642 1.434539
## GE UHC Medicos
## 1.474242 1.597804 1.656630Regresión lineal (Gonzalo Berger)
names(Datacovidof)## [1] "Pais" "Region" "Subregion" "Decesos"
## [5] "Agua" "Saneamiento" "CHE_percapita" "Medicos"
## [9] "UHC" "Camas" "CHE" "CC"
## [13] "PV" "VA" "GE" "RL"
## [17] "RQ" "clusterPAM" "clusterAG" "clusterDIV"
## [21] "dim1" "dim2" "clusterPT" "clustAG"
## [25] "clustDIV" "pam" "agnes" "diana"
## [29] "dbCMD_gob" "clusterAS" "clusterAgnes" "clusterDivisivo"
## [33] "dbCMD_AS"library(stargazer)##
## Please cite as:## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.2. https://CRAN.R-project.org/package=stargazerModelo1=formula(Decesos~CC)
Modelo2=formula(Decesos~CC+PV)
Modelo3=formula(Decesos~CC+PV+VA)
Modelo4=formula(Decesos~CC+PV+VA+GE)
Modelo5=formula(Decesos~CC+PV+VA+GE+RL)
Modelo6=formula(Decesos~CC+PV+VA+GE+RL+RQ)reg1cov=lm(Modelo1,data=Datacovidof)
stargazer(reg1cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant 79.326
## (59.942)
##
## CC 1.520*
## (0.908)
##
## -----------------------------------------------
## Observations 67
## R2 0.041
## Adjusted R2 0.027
## Residual Std. Error 198.333 (df = 65)
## F Statistic 2.806* (df = 1; 65)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01reg2cov=lm(Modelo2,data=Datacovidof)
stargazer(reg2cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant 148.913**
## (61.722)
##
## CC 5.065***
## (1.499)
##
## PV -5.062***
## (1.753)
##
## -----------------------------------------------
## Observations 67
## R2 0.152
## Adjusted R2 0.125
## Residual Std. Error 188.007 (df = 64)
## F Statistic 5.729*** (df = 2; 64)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01reg3cov=lm(Modelo3,data=Datacovidof)
stargazer(reg3cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant 76.591
## (65.436)
##
## CC 1.998
## (1.863)
##
## PV -5.870***
## (1.709)
##
## VA 4.593**
## (1.777)
##
## -----------------------------------------------
## Observations 67
## R2 0.233
## Adjusted R2 0.197
## Residual Std. Error 180.180 (df = 63)
## F Statistic 6.386*** (df = 3; 63)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01reg4cov=lm(Modelo4,data=Datacovidof)
stargazer(reg4cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant 59.779
## (68.770)
##
## CC 0.741
## (2.422)
##
## PV -5.788***
## (1.717)
##
## VA 4.120**
## (1.873)
##
## GE 1.894
## (2.322)
##
## -----------------------------------------------
## Observations 67
## R2 0.241
## Adjusted R2 0.192
## Residual Std. Error 180.660 (df = 62)
## F Statistic 4.930*** (df = 4; 62)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01reg5cov=lm(Modelo5,data=Datacovidof)
stargazer(reg5cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant 65.029
## (73.066)
##
## CC 0.510
## (2.645)
##
## PV -5.816***
## (1.735)
##
## VA 3.940*
## (2.048)
##
## GE 1.619
## (2.636)
##
## RL 0.658
## (2.902)
##
## -----------------------------------------------
## Observations 67
## R2 0.242
## Adjusted R2 0.180
## Residual Std. Error 182.058 (df = 61)
## F Statistic 3.894*** (df = 5; 61)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01reg6cov=lm(Modelo6,data=Datacovidof)
stargazer(reg6cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant 64.627
## (76.997)
##
## CC 0.525
## (2.805)
##
## PV -5.804***
## (1.865)
##
## VA 3.927*
## (2.192)
##
## GE 1.587
## (3.194)
##
## RL 0.636
## (3.186)
##
## RQ 0.047
## (2.597)
##
## -----------------------------------------------
## Observations 67
## R2 0.242
## Adjusted R2 0.166
## Residual Std. Error 183.568 (df = 60)
## F Statistic 3.192*** (df = 6; 60)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01#Análisis de varianza
tanova=anova(reg1cov,reg2cov,reg3cov,reg4cov,reg5cov,reg6cov)
stargazer(tanova,type = 'text',summary = F,title = 'Tabla ANOVA')##
## Tabla ANOVA
## ====================================================
## Res.Df RSS Df Sum of Sq F Pr(> F)
## ----------------------------------------------------
## 1 65 2,556,836.000
## 2 64 2,262,180.000 1 294,655.800 8.744 0.004
## 3 63 2,045,276.000 1 216,904.100 6.437 0.014
## 4 62 2,023,549.000 1 21,726.760 0.645 0.425
## 5 61 2,021,844.000 1 1,704.829 0.051 0.823
## 6 60 2,021,834.000 1 10.907 0.0003 0.986
## ----------------------------------------------------#Regresion lineal parte 2 - Ivonne Mondoñedo Mora
Modelo7=formula(Decesos~CC+PV+VA+GE+RL+RQ+Agua)
Modelo8=formula(Decesos~CC+PV+VA+GE+RL+RQ+Agua+Saneamiento)
Modelo9=formula(Decesos~CC+PV+VA+GE+RL+RQ+Agua+Saneamiento+CHE_percapita)
Modelo10=formula(Decesos~CC+PV+VA+GE+RL+RQ+Agua+Saneamiento+CHE_percapita+Medicos)
Modelo11=formula(Decesos~CC+PV+VA+GE+RL+RQ+Agua+Saneamiento+CHE_percapita+Medicos+UHC)
Modelo12=formula(Decesos~CC+PV+VA+GE+RL+RQ+Agua+Saneamiento+CHE_percapita+Medicos+UHC+Camas)
Modelo13=formula(Decesos~CC+PV+VA+GE+RL+RQ+Agua+Saneamiento+CHE_percapita+Medicos+UHC+Camas+CHE)reg7cov=lm(Modelo7,data=Datacovidof)
stargazer(reg7cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant 638.745
## (658.253)
##
## CC 1.026
## (2.867)
##
## PV -5.863***
## (1.870)
##
## VA 3.749*
## (2.205)
##
## GE 2.055
## (3.244)
##
## RL 0.392
## (3.204)
##
## RQ 0.187
## (2.607)
##
## Agua -6.285
## (7.156)
##
## -----------------------------------------------
## Observations 67
## R2 0.252
## Adjusted R2 0.163
## Residual Std. Error 183.919 (df = 59)
## F Statistic 2.836** (df = 7; 59)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01reg8cov=lm(Modelo8,data=Datacovidof)
stargazer(reg8cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant 780.486
## (793.201)
##
## CC 1.065
## (2.892)
##
## PV -5.829***
## (1.887)
##
## VA 3.929*
## (2.290)
##
## GE 2.081
## (3.270)
##
## RL 0.082
## (3.367)
##
## RQ 0.160
## (2.628)
##
## Agua -9.139
## (11.346)
##
## Saneamiento 1.506
## (4.620)
##
## -----------------------------------------------
## Observations 67
## R2 0.253
## Adjusted R2 0.150
## Residual Std. Error 185.328 (df = 58)
## F Statistic 2.457** (df = 8; 58)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01reg9cov=lm(Modelo9,data=Datacovidof)
stargazer(reg9cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant 596.790
## (780.035)
##
## CC 0.478
## (2.839)
##
## PV -5.667***
## (1.844)
##
## VA 3.753*
## (2.237)
##
## GE 0.627
## (3.277)
##
## RL -0.667
## (3.309)
##
## RQ 0.518
## (2.573)
##
## Agua -6.587
## (11.153)
##
## Saneamiento 1.803
## (4.513)
##
## CHE_percapita 0.029*
## (0.015)
##
## -----------------------------------------------
## Observations 67
## R2 0.300
## Adjusted R2 0.190
## Residual Std. Error 180.935 (df = 57)
## F Statistic 2.719** (df = 9; 57)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01reg10cov=lm(Modelo10,data=Datacovidof)
stargazer(reg10cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant 599.203
## (787.424)
##
## CC 0.502
## (2.878)
##
## PV -5.650***
## (1.872)
##
## VA 3.678
## (2.423)
##
## GE 0.725
## (3.500)
##
## RL -0.655
## (3.341)
##
## RQ 0.476
## (2.640)
##
## Agua -6.636
## (11.266)
##
## Saneamiento 1.868
## (4.615)
##
## CHE_percapita 0.029*
## (0.015)
##
## Medicos -1.904
## (22.291)
##
## -----------------------------------------------
## Observations 67
## R2 0.300
## Adjusted R2 0.176
## Residual Std. Error 182.531 (df = 56)
## F Statistic 2.405** (df = 10; 56)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01reg11cov=lm(Modelo11,data=Datacovidof)
stargazer(reg11cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant -324.635
## (772.156)
##
## CC 0.365
## (2.641)
##
## PV -5.623***
## (1.718)
##
## VA 1.676
## (2.301)
##
## GE -0.694
## (3.238)
##
## RL 0.594
## (3.088)
##
## RQ 1.650
## (2.447)
##
## Agua -3.002
## (10.393)
##
## Saneamiento -2.542
## (4.430)
##
## CHE_percapita 0.013
## (0.015)
##
## Medicos -13.429
## (20.734)
##
## UHC 14.941***
## (4.404)
##
## -----------------------------------------------
## Observations 67
## R2 0.422
## Adjusted R2 0.306
## Residual Std. Error 167.491 (df = 55)
## F Statistic 3.643*** (df = 11; 55)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01reg12cov=lm(Modelo12,data=Datacovidof)
stargazer(reg12cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant -314.188
## (779.538)
##
## CC 0.351
## (2.664)
##
## PV -5.565***
## (1.744)
##
## VA 1.559
## (2.356)
##
## GE -0.696
## (3.266)
##
## RL 0.683
## (3.129)
##
## RQ 1.687
## (2.471)
##
## Agua -3.116
## (10.488)
##
## Saneamiento -2.298
## (4.548)
##
## CHE_percapita 0.013
## (0.015)
##
## Medicos -11.125
## (22.405)
##
## UHC 14.718***
## (4.509)
##
## Camas -3.486
## (12.179)
##
## -----------------------------------------------
## Observations 67
## R2 0.422
## Adjusted R2 0.294
## Residual Std. Error 168.907 (df = 54)
## F Statistic 3.291*** (df = 12; 54)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01reg13cov=lm(Modelo13,data=Datacovidof)
stargazer(reg13cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant -496.129
## (775.065)
##
## CC 0.403
## (2.622)
##
## PV -4.495**
## (1.834)
##
## VA 1.505
## (2.319)
##
## GE -0.840
## (3.215)
##
## RL -0.246
## (3.131)
##
## RQ 2.753
## (2.516)
##
## Agua -3.472
## (10.325)
##
## Saneamiento -2.277
## (4.476)
##
## CHE_percapita -0.006
## (0.018)
##
## Medicos -22.934
## (23.176)
##
## UHC 14.887***
## (4.439)
##
## Camas -1.042
## (12.077)
##
## CHE 25.208
## (15.217)
##
## -----------------------------------------------
## Observations 67
## R2 0.451
## Adjusted R2 0.316
## Residual Std. Error 166.243 (df = 53)
## F Statistic 3.347*** (df = 13; 53)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01tanova_covid=anova(reg1cov,reg2cov,reg3cov,reg4cov,reg5cov,reg6cov,reg7cov,reg8cov,reg9cov,reg10cov,reg11cov,reg12cov,reg13cov)
stargazer(tanova_covid,type = 'text',summary = F,title = 'Tabla ANOVA')##
## Tabla ANOVA
## =====================================================
## Res.Df RSS Df Sum of Sq F Pr(> F)
## -----------------------------------------------------
## 1 65 2,556,836.000
## 2 64 2,262,180.000 1 294,655.800 10.662 0.002
## 3 63 2,045,276.000 1 216,904.100 7.848 0.007
## 4 62 2,023,549.000 1 21,726.760 0.786 0.379
## 5 61 2,021,844.000 1 1,704.829 0.062 0.805
## 6 60 2,021,834.000 1 10.907 0.0004 0.984
## 7 59 1,995,743.000 1 26,090.100 0.944 0.336
## 8 58 1,992,096.000 1 3,647.049 0.132 0.718
## 9 57 1,866,028.000 1 126,068.100 4.562 0.037
## 10 56 1,865,785.000 1 242.979 0.009 0.926
## 11 55 1,542,925.000 1 322,859.900 11.682 0.001
## 12 54 1,540,589.000 1 2,336.810 0.085 0.772
## 13 53 1,464,744.000 1 75,844.720 2.744 0.104
## -----------------------------------------------------DIAGNOSTICO DE LA REGRESIÓN
#linealidad
plot(reg11cov,1)
#Homocedasticidad
library(lmtest)## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericplot(reg11cov,3)
bptest(reg11cov)##
## studentized Breusch-Pagan test
##
## data: reg11cov
## BP = 15.239, df = 11, p-value = 0.1718#Normalidad de residuos
shapiro.test(reg11cov$residuals)##
## Shapiro-Wilk normality test
##
## data: reg11cov$residuals
## W = 0.96983, p-value = 0.1028#multicolinealidad
library(DescTools)##
## Attaching package: 'DescTools'## The following objects are masked from 'package:psych':
##
## AUC, ICC, SDVIF(reg11cov)## CC PV VA GE RL
## 11.869124 3.669769 7.641722 14.814031 16.714802
## RQ Agua Saneamiento CHE_percapita Medicos
## 8.791741 4.304611 4.976820 2.930856 2.094508
## UHC
## 2.653844#Valores influyentes
plot(reg11cov, 5)
checkReg11=as.data.frame(influence.measures(reg11cov)$is.inf)
head(checkReg11)## dfb.1_ dfb.CC dfb.PV dfb.VA dfb.GE dfb.RL dfb.RQ dfb.Agua
## Antigua and Barbuda FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Argentina FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Armenia FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Austria FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Azerbaijan FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Barbados FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## dfb.Snmn dfb.CHE_ dfb.Mdcs dfb.UHC dffit cov.r cook.d hat
## Antigua and Barbuda FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Argentina FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Armenia FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Austria FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Azerbaijan FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## Barbados FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEcheckReg11[checkReg11$cook.d | checkReg11$hat,]## dfb.1_ dfb.CC dfb.PV dfb.VA dfb.GE dfb.RL dfb.RQ dfb.Agua dfb.Snmn
## Cuba FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Haiti FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## dfb.CHE_ dfb.Mdcs dfb.UHC dffit cov.r cook.d hat
## Cuba FALSE FALSE FALSE FALSE TRUE FALSE TRUE
## Haiti FALSE FALSE FALSE FALSE TRUE FALSE TRUEModelo14=formula(Decesos~PV+Agua+Saneamiento+CHE_percapita+Medicos+UHC)
reg14cov=lm(Modelo14,data=Datacovidof)
stargazer(reg14cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant -605.278
## (711.667)
##
## PV -4.092***
## (1.242)
##
## Agua -0.692
## (9.726)
##
## Saneamiento -1.107
## (4.085)
##
## CHE_percapita 0.027**
## (0.012)
##
## Medicos -23.437
## (17.649)
##
## UHC 15.864***
## (4.196)
##
## -----------------------------------------------
## Observations 67
## R2 0.368
## Adjusted R2 0.305
## Residual Std. Error 167.552 (df = 60)
## F Statistic 5.835*** (df = 6; 60)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01tanova_covid=anova(reg11cov,reg14cov)
stargazer(tanova_covid,type = 'text',summary = F,title = 'Tabla ANOVA')##
## Tabla ANOVA
## ====================================================
## Res.Df RSS Df Sum of Sq F Pr(> F)
## ----------------------------------------------------
## 1 55 1,542,925.000
## 2 60 1,684,416.000 -5 -141,490.800 1.009 0.421
## ----------------------------------------------------plot(reg14cov,1)
library(lmtest)
plot(reg14cov,3)
bptest(reg14cov)##
## studentized Breusch-Pagan test
##
## data: reg14cov
## BP = 11.598, df = 6, p-value = 0.07156shapiro.test(reg14cov$residuals)##
## Shapiro-Wilk normality test
##
## data: reg14cov$residuals
## W = 0.94954, p-value = 0.008624library(DescTools)
VIF(reg14cov)## PV Agua Saneamiento CHE_percapita Medicos
## 1.916598 3.767320 4.229073 2.099088 1.516429
## UHC
## 2.407096plot(reg14cov, 5)
checkReg14=as.data.frame(influence.measures(reg14cov)$is.inf)
head(checkReg14)## dfb.1_ dfb.PV dfb.Agua dfb.Snmn dfb.CHE_ dfb.Mdcs dfb.UHC
## Antigua and Barbuda FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Argentina FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Armenia FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Austria FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Azerbaijan FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Barbados FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## dffit cov.r cook.d hat
## Antigua and Barbuda FALSE FALSE FALSE FALSE
## Argentina FALSE FALSE FALSE FALSE
## Armenia FALSE FALSE FALSE FALSE
## Austria FALSE FALSE FALSE FALSE
## Azerbaijan FALSE FALSE FALSE FALSE
## Barbados FALSE FALSE FALSE FALSEcheckReg14[checkReg14$cook.d | checkReg14$hat,]## dfb.1_ dfb.PV dfb.Agua dfb.Snmn dfb.CHE_ dfb.Mdcs dfb.UHC dffit cov.r
## Cuba FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## Haiti FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## cook.d hat
## Cuba FALSE TRUE
## Haiti FALSE TRUEDatacovidof=Datacovidof[-c(19,34),]Modelo14=formula(Decesos~PV+Agua+Saneamiento+CHE_percapita+Medicos+UHC)
reg14cov=lm(Modelo14,data=Datacovidof)
stargazer(reg14cov,type='text',intercept.bottom = F)##
## ===============================================
## Dependent variable:
## ---------------------------
## Decesos
## -----------------------------------------------
## Constant -717.025
## (872.136)
##
## PV -4.154***
## (1.274)
##
## Agua -0.291
## (10.266)
##
## Saneamiento -1.037
## (4.440)
##
## CHE_percapita 0.024*
## (0.013)
##
## Medicos -16.856
## (21.465)
##
## UHC 16.643***
## (4.486)
##
## -----------------------------------------------
## Observations 65
## R2 0.361
## Adjusted R2 0.295
## Residual Std. Error 169.686 (df = 58)
## F Statistic 5.466*** (df = 6; 58)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01plot(reg14cov,1)
library(lmtest)
plot(reg14cov,3)
bptest(reg14cov)##
## studentized Breusch-Pagan test
##
## data: reg14cov
## BP = 10.332, df = 6, p-value = 0.1113shapiro.test(reg14cov$residuals)##
## Shapiro-Wilk normality test
##
## data: reg14cov$residuals
## W = 0.95215, p-value = 0.01359library(DescTools)
VIF(reg14cov)## PV Agua Saneamiento CHE_percapita Medicos
## 1.898414 2.194219 2.447167 2.402427 1.619122
## UHC
## 2.164574plot(reg14cov, 5)
checkReg14=as.data.frame(influence.measures(reg14cov)$is.inf)
head(checkReg14)## dfb.1_ dfb.PV dfb.Agua dfb.Snmn dfb.CHE_ dfb.Mdcs dfb.UHC
## Antigua and Barbuda FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Argentina FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Armenia FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Austria FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Azerbaijan FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Barbados FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## dffit cov.r cook.d hat
## Antigua and Barbuda FALSE FALSE FALSE FALSE
## Argentina FALSE FALSE FALSE FALSE
## Armenia FALSE FALSE FALSE FALSE
## Austria FALSE FALSE FALSE FALSE
## Azerbaijan FALSE FALSE FALSE FALSE
## Barbados FALSE FALSE FALSE FALSEcheckReg14[checkReg14$cook.d | checkReg14$hat,]## dfb.1_ dfb.PV dfb.Agua dfb.Snmn dfb.CHE_ dfb.Mdcs dfb.UHC dffit
## Bolivia FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## United States FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## cov.r cook.d hat
## Bolivia TRUE FALSE TRUE
## United States TRUE FALSE TRUE